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Application of Excitation Moment for Enhancing Fault Diagnosis Probability of Rotating Blade

회전 블레이드의 결함진단 확률제고를 위한 가진 모멘트 적용

  • 김종수 (한양대학교 기계공학부) ;
  • 최찬규 (한양대학교 기계공학부) ;
  • 유홍희 (한양대학교 기계공학부)
  • Received : 2013.11.26
  • Accepted : 2013.12.12
  • Published : 2014.02.01

Abstract

Recently, pattern recognition methods have been widely used by researchers for fault diagnoses of mechanical systems. A pattern recognition method determines the soundness of a mechanical system by detecting variations in the system's vibration characteristics. Hidden Markov models (HMMs) and artificial neural networks (ANNs) have recently been used as pattern recognition methods in various fields. In this study, a HMM-ANN hybrid method for the fault diagnosis of a mechanical system is introduced, and a rotating wind turbine blade with a crack is selected for fault diagnosis. The existence, location, and depth of said crack are identified in this research. For improving the diagnostic accuracy of the method in spite of the presence of noise, a moment with a few specific frequencies is applied to the structure.

Keywords

Hidden Markov Model(HMM);Artificial Neural Network(ANN);Fault Diagnosis;Feature Vector;Vector Quantization

Acknowledgement

Supported by : 한국에너지기술평가원(KETEP)

References

  1. Martin, K. F., 1994, "A Review by Discussion of Monitoring and Fault-diagnosis in Machine-tools," International Journal of Machine Tools and Manufacture, Vol. 34, No. 4, pp. 527-551. https://doi.org/10.1016/0890-6955(94)90083-3
  2. Jardine, A. K. S., Lin, D. and Banjevic, D., 2006, "A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance," Mechanical Systems and Signal Processing, Vol. 20, pp. 1483-1510. https://doi.org/10.1016/j.ymssp.2005.09.012
  3. Rabiner, L. R., Levinson, S. E. and Sondhi, M. M., 1983, "On the Application of Vector Quantization and Hidden Markov Models to Speaker-Independent Isolated Word Recognition," AT&T The System Technical Journal, Vol. 62, No. 4, pp. 1075-1105.
  4. Rabiner, L. R., 1989, "A Tutorial on Hidden Markov Models and Selected Application in Speech Recognition," Proc. IEEE, Vol. 77, No. 2, pp. 257-286. https://doi.org/10.1109/5.18626
  5. Bunks, C., McCarthy, D. and Al-Ani, T., 2000, "Condition-based Maintenance of Machines Using Hidden Markov Models," Mechanical System and Signal Processing, Vol. 14, No. 4, pp. 597-612. https://doi.org/10.1006/mssp.2000.1309
  6. Lee, J. M., Kim, S. J., Hwang, Y. H. and Song, C. S., 2003, "Pattern Recognition of Rotor Fault Signal Using Hidden Markov Model," Trans. Korean Soc. Mech. Eng. A, Vol. 27, No. 11, pp.1864-1872. https://doi.org/10.3795/KSME-A.2003.27.11.1864
  7. Rowley, H. A., Baluja, S. and Kanade, T., 1998, "Neural Network-based Face Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 1, pp. 23-38. https://doi.org/10.1109/34.655647
  8. Samanta, B. and Al-Balushi, K. R, 2003, "Artificial Neural Network Based Fault Diagnostics of Rolling Element Bearings Using Time-domain Features," Mechanical Systems and Signal Processing, Vol. 17, No. 2, pp. 317-328. https://doi.org/10.1006/mssp.2001.1462
  9. Kim, M. K. and Yoo, H. H., 2009, "Vibration Analysis of a Cracked Beam with a Concentrated Mass Undergoing Rotational Motion," Trans. of the KSNVE, Vol. 10, No. 1, pp. 10-16. https://doi.org/10.5050/KSNVN.2009.19.1.010
  10. Robert, M. G., 1984, "Vector Quantization," IEEE ASSP Magazine, pp. 4-28.
  11. Liu, Z., Yin, X, Zhang, Z., Chen, D. and Chen, W., 2004, "Online Rotor Mixed Fault Diagnosis Way Based on Spectrum Analysis of Instantaneous Power in Squirrel Cage Induction Motor," IEEE Transactions on Energy Conversion, Vol. 19, No. 3, pp. 485-490. https://doi.org/10.1109/TEC.2004.832052
  12. Kim, J. S. and Yoo, H. H., 2013, "Fault Diagnosis of a Rotating Blade using HMM/ANN Hybrid Model," Trans. of the KSNVE, Vol. 23, No. 9, pp. 814-822. https://doi.org/10.5050/KSNVE.2013.23.9.814
  13. MATLAB Product Help Manual; Neural Network Tool Box.